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Abstract

Noise removal and lesser computational run time of the digital holographic numerical reconstruction procedure are the critical issues for effective and efficient identification of three-dimensional (3D) particle fields. The present study suggests an improved reconstruction procedure based on the superposition principle. The effectiveness of this proposed method is evaluated using both simulated and experimental data of a 3D particle field. Influence of object-particle number density and sample volume depth on the reconstructed particle field is investigated. There is a reduction in computational run time (as high as 50%) and significant increase in reconstruction effectiveness (as high as 7 times increase) due to the proposed method as compared to the literature (Opt. Express 18, 2426, 2010 and Opt. Express 12, 2270, 2004).

References

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Table 1.

Comparison of Percentage Increase in SNR Values, Percentage Reconstruction Effectiveness (Nr), and Average Particle Diameter (dp) as a Function of Object Particle Density (n0) for Two Different Sample Volume Depths (L) of Both Numerically and Experimentally Generated Holograms

% Increase in SNR

Nr (%)

dp (μm)

Sample Volume Depth L (mm)

n0 (particles/mm3)

Simulation

Experimental

Simulation

Experimental

Simulation (14 μm)

Experimental (14 μm)

1.2

11

38.3

5.7

100

97.4

13.6

15.9

1.2

27

56.6

7.2

97.8

92.6

12.9

17.5

2.8

8

37.4

15.6

98.9

80.3

13.7

18.7

2.8

10

38.7

16.9

98.5

75.2

13.3

18.3

2.8

27

62.2

25.1

89.7

52.5

15.2

19.7

Table 2.

Comparison of Computational Time for Holographic Reconstruction Between the Proposed Algorithm and the Literature [11] as a Function of Object Particle Density (n0) and Sample Volume Depth (L)

Tables (2)

Table 1.

Comparison of Percentage Increase in SNR Values, Percentage Reconstruction Effectiveness (Nr), and Average Particle Diameter (dp) as a Function of Object Particle Density (n0) for Two Different Sample Volume Depths (L) of Both Numerically and Experimentally Generated Holograms

% Increase in SNR

Nr (%)

dp (μm)

Sample Volume Depth L (mm)

n0 (particles/mm3)

Simulation

Experimental

Simulation

Experimental

Simulation (14 μm)

Experimental (14 μm)

1.2

11

38.3

5.7

100

97.4

13.6

15.9

1.2

27

56.6

7.2

97.8

92.6

12.9

17.5

2.8

8

37.4

15.6

98.9

80.3

13.7

18.7

2.8

10

38.7

16.9

98.5

75.2

13.3

18.3

2.8

27

62.2

25.1

89.7

52.5

15.2

19.7

Table 2.

Comparison of Computational Time for Holographic Reconstruction Between the Proposed Algorithm and the Literature [11] as a Function of Object Particle Density (n0) and Sample Volume Depth (L)